Forecasting Patient Visits to Hospitals using a WD&ANN-based Decomposition and Ensemble Model

Forecasting the number of patient visits to hospitals has aroused an increasingly large interest from both theoretic and application perspectives. To enhance the accuracy of forecasting hospital visits, this paper proposes a hybrid approach by coupling wavelet decomposition (WD) and artificial neural network (ANN) under the framework of “decomposition and ensemble”. In this model, the WD is first employed to decompose the original monthly data of the number of patient visits to hospitals into several components and one residual term. Then, the ANN as a powerful prediction tool is implemented to fit each decomposed component and generate individual prediction results. Finally, all individual prediction values are fused into the final prediction output by simple addition method. For illustration and verification, four sets of monthly series data of the number of patient visits to hospitals are used as the sample data, and the results show that the proposed model can obtain significantly more accurate forecasting results than all considered popular forecasting techniques.